A probabilistic approach to latent cluster analysis

Authors:
Zhipeng Xie;Rui Dong;Zhengheng Deng;Zhenying He;Weidong Yang
Affiliations:
School of Computer Science, Fudan University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

Facing a large number of clustering solutions, cluster ensemble method provides an effective approach to aggregating them into a better one. In this paper, we propose a novel cluster ensemble method from probabilistic perspective. It assumes that each clustering solution is generated from a latent cluster model, under the control of two probabilistic parameters. Thus, the cluster ensemble problem is reformulated into an optimization problem of maximum likelihood. An EM-style algorithm is designed to solve this problem. It can determine the number of clusters automatically. Experimenal results have shown that the proposed algorithm outperforms the state-of-the-art methods including EAC-AL, CSPA, HGPA, and MCLA. Furthermore, it has been shown that our algorithm is stable in the predicted numbers of clusters.